OpenRaiser/Intern-Atlas huggingface dataset:
Free-hand Sketch Synthesis with Deformable Stroke Models
Venue: arXiv cs.CV 2015
Year: 2015

We present a generative model which can automatically summarize the stroke composition of free-hand sketches of a given category. When our model is fit to a collection of sketches with similar poses, it discovers and learns the structure and appearance of a set of coherent parts, with each part represented by a group of strokes. It represents both consistent (topology) as well as diverse aspects (structure and appearance variations) of each sketch category. Key to the success of our model are important insights learned from a comprehensive study performed on human stroke data. By fitting this model to images, we are able to synthesize visually similar and pleasant free-hand sketches.
Feedforward Sequential Memory Neural Networks without Recurrent Feedback
Venue: arXiv cs.NE 2015
Year: 2015

We introduce a new structure for memory neural networks, called feedforward sequential memory networks (FSMN), which can learn long-term dependency without using recurrent feedback. The proposed FSMN is a standard feedforward neural networks equipped with learnable sequential memory blocks in the hidden layers. In this work, we have applied FSMN to several language modeling (LM) tasks. Experimental results have shown that the memory blocks in FSMN can learn effective representations of long history. Experiments have shown that FSMN based language models can significantly outperform not only feedforward neural network (FNN) based LMs but also the popular recurrent neural network (RNN) LMs.
An Improved Combinatorial Polynomial Algorithm for the Linear Arrow-Debreu Market
Venue: arXiv cs.DS 2015
Year: 2015

We present an improved combinatorial algorithm for the computation of equilibrium prices in the linear Arrow-Debreu model. For a market with $n$ agents and integral utilities bounded by $U$, the algorithm runs in $O(n^7 \log^3 (nU))$ time. This improves upon the previously best algorithm of Ye by a factor of $\tOmega(n)$. The algorithm refines the algorithm described by Duan and Mehlhorn and improves it by a factor of $\tOmega(n^3)$. The improvement comes from a better understanding of the iterative price adjustment process, the improved balanced flow computation for nondegenerate instances, and a novel perturbation technique for achieving nondegeneracy.
Wavelet Frame Based Image Restoration Using Sparsity, Nonlocal and Support Prior of Frame Coefficients
Venue: arXiv cs.CV 2015
Year: 2015

The wavelet frame systems have been widely investigated and applied for image restoration and many other image processing problems over the past decades, attributing to their good capability of sparsely approximating piece-wise smooth functions such as images. Most wavelet frame based models exploit the $l_1$ norm of frame coefficients for a sparsity constraint in the past. The authors in \cite{ZhangY2013, Dong2013} proposed an $l_0$ minimization model, where the $l_0$ norm of wavelet frame coefficients is penalized instead, and have demonstrated that significant improvements can be achieved compared to the commonly used $l_1$ minimization model. Very recently, the authors in \cite{Chen2015} proposed $l_0$-$l_2$ minimization model, where the nonlocal prior of frame coefficients is incorporated. This model proved to outperform the single $l_0$ minimization based model in terms of better recovered image quality. In this paper, we propose a truncated $l_0$-$l_2$ minimization model which combines sparsity, nonlocal and support prior of the frame coefficients. The extensive experiments have shown that the recovery results from the proposed regularization method performs better than existing state-of-the-art wavelet frame based methods, in terms of edge enhancement and texture preserving performance.
Learn to Evaluate Image Perceptual Quality Blindly from Statistics of Self-similarity
Venue: arXiv cs.CV 2015
Year: 2015

Among the various image quality assessment (IQA) tasks, blind IQA (BIQA) is particularly challenging due to the absence of knowledge about the reference image and distortion type. Features based on natural scene statistics (NSS) have been successfully used in BIQA, while the quality relevance of the feature plays an essential role to the quality prediction performance. Motivated by the fact that the early processing stage in human visual system aims to remove the signal redundancies for efficient visual coding, we propose a simple but very effective BIQA method by computing the statistics of self-similarity (SOS) in an image. Specifically, we calculate the inter-scale similarity and intra-scale similarity of the distorted image, extract the SOS features from these similarities, and learn a regression model to map the SOS features to the subjective quality score. Extensive experiments demonstrate very competitive quality prediction performance and generalization ability of the proposed SOS based BIQA method.
Per-Antenna Constant Envelope Precoding for Secure Transmission in Large-Scale MISO Systems
Venue: arXiv cs.IT 2015
Year: 2015

Secure transmission in large-scale MISO systems employing artificial noise (AN) is studied under the per-antenna constant envelope (CE) constraint. Achievable secrecy rate of the per-antenna CE precoding scheme for large-scale MISO is analyzed and compared with that of the matched filter linear precoding. A two-stage per-antenna CE precoding scheme for joint signal-plus-AN transmission is proposed. The first stage determines the per-antenna CE precoding for the information-bearing signal. A properly generated AN using an iteration algorithm is incorporated into the transmit signal in the second stage such that the combined signal-plus-AN satisfies the per-antenna CE constraint and the AN is orthogonal to the user channel. It is shown that compared to conventional per-antenna CE transmission, this joint signal-plus-AN secure transmission scheme does not require additional transmit power. An alternative low-complexity AN generation scheme which uses a separate antenna to cancel the AN leakage to the intended user introduced by randomly generated AN is also proposed.
DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations
Venue: arXiv cs.CV 2015
Year: 2015

Understanding and predicting the human visual attentional mechanism is an active area of research in the fields of neuroscience and computer vision. In this work, we propose DeepFix, a first-of-its-kind fully convolutional neural network for accurate saliency prediction. Unlike classical works which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant which prevents them from modeling location dependent patterns (e.g. centre-bias). Our network overcomes this limitation by incorporating a novel Location Biased Convolutional layer. We evaluate our model on two challenging eye fixation datasets -- MIT300, CAT2000 and show that it outperforms other recent approaches by a significant margin.
On The Number of Optimal Linear Index Codes For Unicast Index Coding Problems
Venue: arXiv cs.IT 2015
Year: 2015

An index coding problem arises when there is a single source with a number of messages and multiple receivers each wanting a subset of messages and knowing a different set of messages a priori. The noiseless Index Coding Problem is to identify the minimum number of transmissions (optimal length) to be made by the source through noiseless channels so that all receivers can decode their wanted messages using the transmitted symbols and their respective prior information. Recently, it is shown that different optimal length codes perform differently in a noisy channel. Towards identifying the best optimal length index code one needs to know the number of optimal length index codes. In this paper we present results on the number of optimal length index codes making use of the representation of an index coding problem by an equivalent network code. Our formulation results in matrices of smaller sizes compared to the approach of Kotter and Medard. Our formulation leads to a lower bound on the minimum number of optimal length codes possible for all unicast index coding problems which is met with equality for several special cases of the unicast index coding problem. A method to identify the optimal length codes which lead to minimum-maximum probability of error is also presented.
OmniGraph: Rich Representation and Graph Kernel Learning
Venue: arXiv cs.CL 2015
Year: 2015

OmniGraph, a novel representation to support a range of NLP classification tasks, integrates lexical items, syntactic dependencies and frame semantic parses into graphs. Feature engineering is folded into the learning through convolution graph kernel learning to explore different extents of the graph. A high-dimensional space of features includes individual nodes as well as complex subgraphs. In experiments on a text-forecasting problem that predicts stock price change from news for company mentions, OmniGraph beats several benchmarks based on bag-of-words, syntactic dependencies, and semantic trees. The highly expressive features OmniGraph discovers provide insights into the semantics across distinct market sectors. To demonstrate the method's generality, we also report its high performance results on a fine-grained sentiment corpus.
Integral Approximations for Coverage Probability
Venue: arXiv cs.IT 2015
Year: 2015

This letter gives approximations to an integral appearing in the formula for downlink coverage probability of a typical user in Poisson point process (PPP) based stochastic geometry frameworks of the form $\int_0^\infty \exp\{ - (Ax + B x^{α/2}) \} \ud x$. Four different approximations are studied. For systems that are interference-limited or noise-limited, conditions are identified when the approximations are valid. For intermediate cases, we recommend the use of Laplace approximation. Numerical results validate the accuracy of the approximations.
Textual Analysis for Studying Chinese Historical Documents and Literary Novels
Venue: arXiv cs.CL 2015
Year: 2015

We analyzed historical and literary documents in Chinese to gain insights into research issues, and overview our studies which utilized four different sources of text materials in this paper. We investigated the history of concepts and transliterated words in China with the Database for the Study of Modern China Thought and Literature, which contains historical documents about China between 1830 and 1930. We also attempted to disambiguate names that were shared by multiple government officers who served between 618 and 1912 and were recorded in Chinese local gazetteers. To showcase the potentials and challenges of computer-assisted analysis of Chinese literatures, we explored some interesting yet non-trivial questions about two of the Four Great Classical Novels of China: (1) Which monsters attempted to consume the Buddhist monk Xuanzang in the Journey to the West (JTTW), which was published in the 16th century, (2) Which was the most powerful monster in JTTW, and (3) Which major role smiled the most in the Dream of the Red Chamber, which was published in the 18th century. Similar approaches can be applied to the analysis and study of modern documents, such as the newspaper articles published about the 228 incident that occurred in 1947 in Taiwan.
Towards non-threaded Concurrent Constraint Programming for implementing multimedia interaction systems
Venue: arXiv cs.MM 2015
Year: 2015

In this work we explain the implementation of event-driven real-time interpreters for the Concurrent Constraint Programming (CCP) and Non-deterministic Timed Concurrent Constraint (NTCC) for- malisms. The CCP interpreter was tested with a program to find, concurrently, paths in a graph and it will be used in the future to find musical sequences in the music improvisation software Omax, developed by the French Acoustics/Music Research Institute (IRCAM). In the other hand, the NTCC interpreter was tested with a music improvisation system based on NTCC (CCFOMI), developed by the AVISPA research group and IRCAM. Additionally, we present GECOL 2, a wrapper for the Generic Constraints Development Environment (GECODE) to Common LISP, de- veloped to port the interpreters to Common LISP in the future. We concluded that using GECODE for the concurrency control avoids the need of having threads and synchronizing them, leading to a simple and efficient implementation of CCP and NTCC. We also noticed that the time units in NTCC interpreter do not represent discrete time units, because when we simulate the NTCC specifications in the interpreter, the time units have different durations. In the future, we propose forcing the duration of each time unit to a fix time, that way we would be able to reason about NTCC time units as we do with discrete time units.
Influence of network topology on cooperative problem-solving systems
Venue: arXiv cs.SI 2015
Year: 2015

The idea of a collective intelligence behind the complex natural structures built by organisms suggests that the organization of social networks is selected so as to optimize problem-solving competence at the group-level. Here we study the influence of the social network topology on the performance of a group of agents whose task is to locate the global maxima of NK fitness landscapes. Agents cooperate by broadcasting messages informing on their fitness and use this information to imitate the fittest agent in their influence networks. In the case those messages convey accurate information on the proximity of the solution (i.e., for smooth fitness landscapes) we find that high connectivity as well as centralization boost the group performance. For rugged landscapes, however, these characteristics are beneficial for small groups only. For large groups, it is advantageous to slow down the information transmission through the network to avoid local maximum traps. Long-range links and modularity have marginal effects on the performance of the group, except for a very narrow region of the model parameters.
End-to-End Error-Correcting Codes on Networks with Worst-Case Symbol Errors
Venue: arXiv cs.IT 2015
Year: 2015

The problem of coding for networks experiencing worst-case symbol errors is considered. We argue that this is a reasonable model for highly dynamic wireless network transmissions. We demonstrate that in this setup prior network error-correcting schemes can be arbitrarily far from achieving the optimal network throughput. A new transform metric for errors under the considered model is proposed. Using this metric, we replicate many of the classical results from coding theory. Specifically, we prove new Hamming-type, Plotkin-type, and Elias-Bassalygo-type upper bounds on the network capacity. A commensurate lower bound is shown based on Gilbert-Varshamov-type codes for error-correction. The GV codes used to attain the lower bound can be non-coherent, that is, they do not require prior knowledge of the network topology. We also propose a computationally-efficient concatenation scheme. The rate achieved by our concatenated codes is characterized by a Zyablov-type lower bound. We provide a generalized minimum-distance decoding algorithm which decodes up to half the minimum distance of the concatenated codes. The end-to-end nature of our design enables our codes to be overlaid on the classical distributed random linear network codes [1]. Furthermore, the potentially intensive computation at internal nodes for the link-by-link error-correction is un-necessary based on our design.
Elastic Resource Allocation for Distributed Graph Processing Platforms
Venue: arXiv cs.DC 2015
Year: 2015

Distributed graph platforms like Pregel have used vertex- centric programming models to process the growing corpus of graph datasets using commodity clusters. The irregular structure of graphs cause load imbalances across machines operating on graph partitions, and this is exacerbated for non-stationary graph algorithms such as traversals, where not all parts of the graph are active at the same time. As a result, such graph platforms, even as they scale, do not make efficient use of distributed resources. Clouds offer elastic virtual machines that can be leveraged to improve the resource utilization for such platforms and hence reduce the monetary cost for their execution. In this paper, we propose strategies for elastic placement of graph partitions on Cloud VMs for subgraphcentric programming model to reduce the cost of execution compared to a static placement, even as we minimize the increase in makespan. These strategies are innovative in modeling the graph algorithms behavior. We validate our strategies for several graphs, using runtime tra- ces for their distributed execution of a Breadth First Search (BFS) algorithms on our subgraph-centric GoFFish graph platform. Our strategies are able to reduce the cost of exe- cution by up to 42%, compared to a static placement, while achieving a makespan that is within 29% of the optimal
Data structuring for the ontological modelling of wind energy systems
Venue: arXiv cs.AI 2015
Year: 2015

Small wind projects encounter difficulties to be efficiently deployed, partly because wrong way data and information are managed. Ontologies can overcome the drawbacks of partially available, noisy, inconsistent, and heterogeneous data sources, by providing a semantic middleware between low level data and more general knowledge. In this paper, we engineer an ontology for the wind energy domain using description logic as technical instrumentation. We aim to integrate corpus of heterogeneous knowledge, both digital and human, in order to help the interested user to speed-up the initialization of a small-scale wind project. We exemplify one use case scenario of our ontology, that consists of automatically checking whether a planned wind project is compliant or not with the active regulations.
Parallel Triangles Counting Using Pipelining
Venue: arXiv cs.DC 2015
Year: 2015

The generalized method to have a parallel solution to a computational problem, is to find a way to use Divide & Conquer paradigm in order to have processors acting on its own data and therefore all can be scheduled in parallel. MapReduce is an example of this approach: Input data is transformed by the mappers, in order to feed the reducers that can run in parallel. In general this schema gives efficient problem solutions, but it stops being true when the replication factor grows. We present another program schema that is useful for describing problem solutions, that can exploit dynamic pipeline parallelism without having to deal with replica- tion factors. We present the schema in an example: counting triangles in graphs, in particular when the graph do not fit in memory. We describe the solution in NiMo, a graphical programming language that implements the implicitly functional parallel data ow model of computation. The solution obtained using NiMo, is architecture agnostic and can be deployed in any parallel/distributed architecture adapting dynamically the processor usage to input characteristics.
LiteMat: a scalable, cost-efficient inference encoding scheme for large RDF graphs
Venue: arXiv cs.DB 2015
Year: 2015

The number of linked data sources and the size of the linked open data graph keep growing every day. As a consequence, semantic RDF services are more and more confronted with various "big data" problems. Query processing in the presence of inferences is one them. For instance, to complete the answer set of SPARQL queries, RDF database systems evaluate semantic RDFS relationships (subPropertyOf, subClassOf) through time-consuming query rewriting algorithms or space-consuming data materialization solutions. To reduce the memory footprint and ease the exchange of large datasets, these systems generally apply a dictionary approach for compressing triple data sizes by replacing resource identifiers (IRIs), blank nodes and literals with integer values. In this article, we present a structured resource identification scheme using a clever encoding of concepts and property hierarchies for efficiently evaluating the main common RDFS entailment rules while minimizing triple materialization and query rewriting. We will show how this encoding can be computed by a scalable parallel algorithm and directly be implemented over the Apache Spark framework. The efficiency of our encoding scheme is emphasized by an evaluation conducted over both synthetic and real world datasets.
Interference Suppression in Multiuser Systems Based on Bidirectional Algorithms
Venue: arXiv cs.IT 2015
Year: 2015

This paper presents adaptive bidirectional minimum mean-square error parameter estimation algorithms for fast-fading channels. The time correlation between successive channel gains is exploited to improve the estimation and tracking capabilities of adaptive algorithms and provide robustness against time-varying channels. Bidirectional normalized least mean-square and conjugate gradient algorithms are devised along with adaptive mixing parameters that adjust to the time-varying channel correlation properties. An analysis of the proposed algorithms is provided along with a discussion of their performance advantages. Simulations for an application to interference suppression in multiuser DS-CDMA systems show the advantages of the proposed algorithms.
Effective Capacity of Buffer-Aided Full-Duplex Relay Systems with Selection Relaying
Venue: arXiv cs.IT 2015
Year: 2015

In this work, the achievable rate of three-node relay systems with selection relaying under statistical delay constraints, imposed on the limitations of the maximum end-to-end delay violation probabilities, is investigated. It is assumed that there are queues of infinite size at both the source and relay node, and the source can select the relay or destination for data reception. Given selection relaying policy, the effective bandwidth of the arrival processes of the queue at the relay is derived. Then, the maximum constant arrival rate can be identified as the maximum effective capacity as a function of the statistical end-to-end queueing delay constraints, signal-to-noise ratios (SNR) at the source and relay, the fading distributions of the links, and the relay policy. Subsequently, a relay policy that incorporates the statistical delay constraints is proposed. It is shown that the proposed relay policy can achieve better performance than existing protocols. Moreover, it is demonstrated that buffering relay model can still help improve the throughput of relay systems in the presence of statistical delay constraints and source-destination link.
An indicator of journal impact that is based on calculating a journal's percentage of highly cited publications
Venue: arXiv cs.DL 2015
Year: 2015

The two most used citation impact indicators in the assessment of scientific journals are, nowadays, the impact factor and the h-index. However, both indicators are not field normalized (vary heavily depending on the scientific category) which makes them incomparable between categories. Furthermore, the impact factor is not robust to the presence of articles with a large number of citations, while the h-index depends on the journal size. These limitations are very important when comparing journals of different sizes and categories. An alternative citation impact indicator is the percentage of highly cited articles in a journal. This measure is field normalized (comparable between scientific categories), independent of the journal size and also robust to the presence of articles with a high number of citations. This paper empirically compares this indicator with the impact factor and the h-index, considering different time windows and citation percentiles (levels of citation for considering an article as highly cited compared to others in the same year and category).